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Related Concept Videos

Pulmonary Embolism II: Diagnostic Studies and Interprofessional Care01:29

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Diagnosing Pulmonary EmbolismDiagnosing pulmonary embolism (PE) involves clinical assessment and advanced imaging tests. The preferred diagnostic tool is the spiral (helical) CT scan or CT angiography (CTA), which uses intravenous contrast media to visualize the pulmonary vasculature and identify emboli.A ventilation-perfusion (V/Q) scan is an alternative for patients unable to receive contrast media. This scan includes both perfusion and ventilation scanning. Perfusion scanning involves...
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Pulmonary embolism (PE) occurs when a thrombus, fat or air embolus, amniotic fluid, or tumor tissue blocks one or more pulmonary arteries. These blockages originate in the venous system or the right side of the heart.EtiologyPE primarily arises from deep vein thrombosis (DVT) and other hypercoagulable states, such as inherited thrombophilias. Additional etiological factors include venous stasis, commonly seen in obesity, and endothelial injury from surgery and trauma. Less common causes include...
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A pulmonary embolism occurs when a thrombus, amniotic fluid, tumor tissue, fat, or air embolus blocks one or more pulmonary arteries. Effective nursing management and patient education are crucial for improving outcomes and preventing recurrence.Nursing management starts with obtaining a comprehensive patient history, particularly noting any history of deep vein thrombosis (DVT). Assess for clinical manifestations, including dyspnea, chest pain, crackles, heart murmurs, and signs of right-sided...
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An efficient machine learning framework to identify important clinical features associated with pulmonary embolism.

Baiming Zou1,2, Fei Zou1, Jianwen Cai1

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America.

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Accurately diagnosing pulmonary embolism (PE) is vital. A new deep learning framework, PermFIT-DNN, effectively identifies key clinical features for PE risk assessment, improving patient outcomes.

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Area of Science:

  • Medical diagnostics
  • Artificial intelligence in healthcare
  • Pulmonary medicine

Background:

  • Misdiagnosis of pulmonary embolism (PE) carries severe risks, including death.
  • Accurate identification of PE clinical features is challenging due to complex influencing factors.
  • Distinguishing PE from asthma exacerbation requires careful feature analysis.

Purpose of the Study:

  • To develop and validate an effective framework for identifying key clinical features of pulmonary embolism.
  • To improve the accuracy of PE diagnosis, especially in patients with asthma exacerbation symptoms.
  • To aid in the prompt identification of potential PE patients, including those presenting asymptomatically.

Main Methods:

  • Utilized a deep neural network (DNN) model combined with a permutation-based feature importance test (PermFIT).
  • The integrated framework, named PermFIT-DNN, was applied to analyze data from a PE study involving asthma exacerbation patients.
  • Permutation-based feature importance was employed to robustly identify critical diagnostic indicators.

Main Results:

  • The PermFIT-DNN framework successfully identified key features crucial for classifying PE status.
  • The identified features demonstrated robustness in distinguishing PE from other conditions.
  • The framework showed potential in accurately predicting the risk of pulmonary embolism.

Conclusions:

  • The PermFIT-DNN framework offers a powerful tool for identifying critical features in PE diagnosis.
  • This approach can enhance clinical practice by improving the accuracy and timeliness of PE identification.
  • Accurate feature identification aids in risk stratification and personalized patient management for pulmonary embolism.